Currency has utmost significance in our day to day life. Numerous types of the currency exist throughout the world. As an example, types of currency may be rupee, dollar, euro and the like. The currency may vary from country to country in terms of color, size, shape, thickness and other appearance based properties. Further, each type of currency may include different denominations and each denomination may include different versions/forms. Due to the presence of the numerous types of the currency, denominations and versions/forms, counting different denomination notes in a bunch is a challenging as well as a tedious task. Further, recognizing the currencies of various countries is also difficult.
To overcome the challenge of recognizing the currencies, paper currency recognition systems based on pattern recognition were developed. The paper currency recognition systems work in combination with automated cash handling machines to automatically recognize the currencies, count the currencies and convert into other currencies without human supervision. However, the paper currency recognition systems may recognize only pre-stored denominations of the currencies and pre-stored versions/forms of the denominations. Therefore, such machines may not be able to update new denominations of the currencies and new versions/forms of the new denominations dynamically, thereby leading to delay in performing operations such as counting the currencies, converting into other currencies and the like. Further, since different currencies have different security features, developing a single system for recognizing any type of currency is a challenge. Alternatively, developing a separate system for recognizing each type of currency is also a tedious process.
Existing techniques include a method of counting and validating the value of the currency by sensing at least one coded tag printed on the currency document. By sensing the coded tag, a unique identity of the currency may be obtained based on which a value corresponding to the currency may be determined. A few other existing techniques recognize the currency based on probabilistic neural networks. However, the existing techniques concentrate only on differentiating currency documents such as bank notes within a single nation or limited number of nations for limited types of currency based on criteria like denomination, year of printing and the like. Further, the existing techniques concentrate on sorting the currency documents of a particular nationality based on the denomination. None of the existing techniques concentrate on managing currency documents whose nationality is unknown to the system.